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Classification of brain electrophysiological changes in response to colour stimuli

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Abstract

In this study, the classification of ongoing brain activity occurring as a response to colour stimuli was managed and reported. Until now, the classification of the seen colour from brain electrical signals has not been investigated or reported in the related literature. In this study, we aimed to classify EEG brain responses corresponding to blue, green, and red coloured shapes. In addition to the current literature, we focused on ongoing EEG responses instead of using ERP metrics, with visual stimulus-related ERP metrics also compared throughout the study. The feature extraction process was carried out using the Fourier transform to obtain the conventional band power values of the EEG for each stimulus type. Delta, theta, alpha, beta, and gamma-band power values of each one-second period constituted the feature set. In addition to scalp measurements, a second feature set was obtained based on the inverse solution of the EEG waves. Furthermore, we applied one-way ANOVA for the feature selection prior to classification procedures. Four classifiers were implemented using the reduced feature set and the raw one as well. The differences between scalp responses were localized mainly around the temporal and temporoparietal regions. Our ERP-component findings support the fact that additional brain regions among the visual cortex participate in the colour categorization process of the brain. RGB colours were identified using 1 s EEG data. Ensemble-KNN and KNN achieved the highest accuracy values (93%) when used either with scalp spectral features or source space features.

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References

  1. Hubel DH (1995) Eye, Brain, and Vision, ISBN: 0716750201,9780716750208

  2. Kaiser PK (1984) Physiological response to color: a critical review. Color Res Appl 9(1):29–36. https://doi.org/10.1002/col.5080090106

    Article  Google Scholar 

  3. Yoto A, Katsuura T, Iwanaga K, Shimomura Y (2007) Effects of object color stimuli on human brain activities in perception and attention referred to EEG alpha band response. J Physiol Anthropol 26(3):373–379. https://doi.org/10.2114/jpa2.26.373

    Article  PubMed  Google Scholar 

  4. Zeki S, Watson JD, Lueck CJ, Friston KJ, Kennard C, Frackowiak RS (1991) A direct demonstration of functional specialization in human visual cortex. J Neurosci 11(3):641–649. https://doi.org/10.1523/jneurosci.11-03-00641.1991

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Liu CS, Bryan RN, Miki A, Woo JH, Liu GT, Elliott MA (2006) Magnocellular and parvocellular visual pathways have different blood oxygen level-dependent signal time courses in human primary visual cortex. Am J Neuroradiol 27(8):1628–1634

    PubMed  PubMed Central  Google Scholar 

  6. Remington LA (2012) Visual pathway. Clinical anatomy and physiology of the visual system, 3rd edn. Elsevier Inc, Amsterdam, pp 233–252

    Chapter  Google Scholar 

  7. Lloyd-Jones TJ, Roberts MV, Leek EC, Fouquet NC, Truchanowicz EG (2012) The time course of activation of object shape and shape+colour representations during memory retrieval. PLoS ONE. https://doi.org/10.1371/journal.pone.0048550

    Article  PubMed  PubMed Central  Google Scholar 

  8. Münch M, Plomp G, Thunell E, Kawasaki A, Scartezzini JL, Herzog MH (2014) Different colors of light lead to different adaptation and activation as determined by high-density EEG. Neuroimage 1(101):547–554. https://doi.org/10.1016/j.neuroimage.2014.06.071

    Article  Google Scholar 

  9. Al Mahbubi NR, Fikri MY, Rahmania V et al (2019) Color detection with brain wave (mind wave) for disabilities people using FFT and deep learning method. 2019 International symposium on electronics and smart devices (ISESD). IEEE, New York, pp 1–4. https://doi.org/10.1109/ISESD.2019.8909452

    Chapter  Google Scholar 

  10. Alharbi ET, Rasheed S, Buhari SM (2016) Feature selection algorithm for evoked EEG signal due to RGB colors. 2016 9th International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI). IEEE, New York, pp 1503–1520. https://doi.org/10.1109/CISP-BMEI.2016.7852955

    Chapter  Google Scholar 

  11. Dechwechprasit P, Phothisonothai M, Tantisatirapong S (2016) Time-frequency analysis of red-green visual flickers based on steady-state visual evoked potential recording. 2016 9th Biomedical engineering international conference (BMEiCON). IEEE, New York, pp 1–4. https://doi.org/10.1109/BMEiCON.2016.7859643

    Chapter  Google Scholar 

  12. Liu X, Hong K (2016) Three class classification of fNIRS signals for the detection of RGB color stimuli in the visual cortex. 2016 16th IEEE international conference on control, automation and systems (ICCAS). IEEE, New York, pp 1107–1111. https://doi.org/10.1109/ICCAS.2016.7832449

    Chapter  Google Scholar 

  13. Babiloni C, Barry RJ, Basar E, Blinowska KJ, Cichocki A, Drinkenburg WHIM, Klimesch W, Knight RT, da Silva FL, Nunez P, Oostenveld R, Jeong J, Pascual-Marqui R, Valdes-Sosa P, Hallett M (2020) International federation of clinical neurophysiology (IFCN)—EEG research workgroup: recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: applications in clinical research studies. Clin Neurophysiol 131(1):285–307. https://doi.org/10.1016/j.clinph.2019.06.234

    Article  PubMed  Google Scholar 

  14. Koivisto M, Revonsuo A (2009) Event-related brain potential correlates of visual awareness. Neurosci Biobehav Rev 34(6):922–934. https://doi.org/10.1016/j.neubiorev.2009.12.002

    Article  PubMed  Google Scholar 

  15. Rutman AM, Clapp WC, Chadick JZ, Gazzaley A (2010) Early top-down control of visual processing predicts working memory performance. J Cogn Neurosci 22(6):1224–1234. https://doi.org/10.1162/jocn.2009.21257

    Article  PubMed  PubMed Central  Google Scholar 

  16. Radhakrishnan R, Addy PH, Sewell RA, Skosnik PD, Ranganathan M, D’Souza DC (2014) Cannabis, cannabinoids, and the association with psychosis. In: Madras Bertha, Kuhar Michael (eds) The effects of drug abuse on the human nervous system. Academic Press, Cambridge, pp 423–474

    Chapter  Google Scholar 

  17. Polich J, Ellerson PC, Cohen J (1996) P300, stimulus intensity, modality, and probability. Int J Psychophysiol 23(1–2):55–62

    Article  CAS  Google Scholar 

  18. Blanco JA, Vanleer AC, Calibo TK, Firebaugh SL (2019) Single-trial cognitive stress classification using portable wireless electroencephalography. Sensors (Basel) 19(3):499. https://doi.org/10.3390/s19030499

    Article  Google Scholar 

  19. Semmlow John (2012) The Fourier transform and power spectrum: implications and applications. In: Semmlow John (ed) Biomedical engineering, signals and systems for bioengineers, 2nd edn. Academic Press, Cambridge, pp 131–165

    Chapter  Google Scholar 

  20. Pascual-Marqui RD (2002) Standardized low resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 24D:5–12

    Google Scholar 

  21. Duru AD, Balcıoğlu TH, Çakır Ö et al (2019) Acute changes in electrophysiological brain dynamics in elite karate players. Iran J Sci Technol Trans Electr Eng 44:565–579. https://doi.org/10.1007/s40998-019-00252-0

    Article  Google Scholar 

  22. Rajimehr R, Tootell R (2008) Organization of human visual cortex. The senses: a comprehensive reference. Elsevier, Amsterdam, pp 595–614. https://doi.org/10.1016/B978-012370880-9.00292-9

    Chapter  Google Scholar 

  23. Osborne BJ, Liu GT, Newman NJ (2007) Cranial nerve II and afferent visual pathways. In: Goetz CG (ed) Textbook of clinical neurology, 3rd edn. W. B. Saunders, Philadelphia, pp 113–132. https://doi.org/10.1016/B978-141603618-0.10008-6

    Chapter  Google Scholar 

  24. Remington LA (2012) Visual pathway. In: Remington LA (ed) Clinical anatomy and physiology of the visual system. Butterworth-Heinemann, Oxford, pp 233–252. https://doi.org/10.1016/B978-1-4377-1926-0.10013-X

    Chapter  Google Scholar 

  25. Siok WT, Kay P, Wange WSY, Chan AHD, Chenf L, Luke K, Tana LH (2009) Language regions of brain are operative in color perception. PNAS 106(20):8140–8145. https://doi.org/10.1073/pnas.0903627106

    Article  Google Scholar 

  26. Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2):229–244. https://doi.org/10.1007/s12021-013-9204-3

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. CSBJ 13:8–17. https://doi.org/10.1016/j.csbj.2014.11.005

    Article  CAS  PubMed  Google Scholar 

  28. Ben-Hur A, Weston J (2010) A User’s guide to support vector machines. In: Carugo O, Eisenhaber F (eds) data mining techniques for the life sciences, methods in molecular biology. Humana Press, Totowa, pp 223–239. https://doi.org/10.1007/978-1-60327-241-4_13 (609)

    Chapter  Google Scholar 

  29. Tharwat A (2016) Linear vs. quadratic discriminant analysis classifier: a tutorial. Intl J Appl Pattern Recognit. https://doi.org/10.1504/IJAPR.2016.079050

    Article  Google Scholar 

  30. Friederici AD (2011) The brain basis of language processing: from structure to function. Physiol Rev 91:1357–1392. https://doi.org/10.1152/physrev.00006.2011

    Article  PubMed  Google Scholar 

  31. Bird CM, Berens SC, Horner AJ, Franklin A (2014) Categorical encoding of color in the brain. PNAS 111(12):4590–4595. https://doi.org/10.1073/pnas.1315275111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Siok WT, Kay P, Wang WSY, Chan AHD, Chen L, Luke KK, Hai LN (2009) Language regions of brain are operative in color perception. Proc Nat Acad Sci 106(20):8140–8145. https://doi.org/10.1073/pnas.0903627106

    Article  Google Scholar 

  33. Mazoyer B, Zago L, Jobard G, Crivello F, Joliot M, Perchey G, Mellet E, Petit L, Tzourio-Mazoyer N (2014) Gaussian mixture modeling of hemispheric lateralization for language in a large sample of healthy individuals balanced for handedness. PLoS ONE 9(6):e101165. https://doi.org/10.1371/journal.pone.0101165

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yu JH, Sim KB (2016) Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram. Optik. https://doi.org/10.1016/j.ijleo.2016.07.074

    Article  PubMed  PubMed Central  Google Scholar 

  35. Blanco JA, Vanleer AC, Calibo TK, Firebaugh SL (2019) Single-trial cognitive stress classification using portable wireless electroencephalography. Sensors (Basel). 19(3):499. https://doi.org/10.3390/s19030499

    Article  PubMed Central  Google Scholar 

  36. Phillips K, Fosu O, Jouny I (2015) Separation and classification of EEG responses to color stimuli. 2015 41st Annual Northeast biomedical engineering conference (NEBEC). IEEE, New York, pp 1–2. https://doi.org/10.1109/nebec.2015.7117185

    Chapter  Google Scholar 

  37. Kapeller C, Ogawa H, Schalk G, Kunii N, Coon WG, Scharinger J, Guger C, Kamada K (2018) Real-time detection and discrimination of visual perception using electrocorticographic signals. J Neural Eng 15(3):036001. https://doi.org/10.1088/1741-2552/aaa9f6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Claeys KG, Dupont P, Cornette L, Sunaert S, Van Hecke P, De Schutter E, Orban GA (2004) Color discrimination involves ventral and dorsal stream visual areas. Cereb Cortex 14:803–822. https://doi.org/10.1093/cercor/bhh040

    Article  PubMed  Google Scholar 

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Acknowledgements

The research is partly supported by Istanbul Development Agency (ISTKA) under Project ID TR10/18/GMP/0032.

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Authors and Affiliations

  1. Department of Molecular Biotechnology, Faculty of Science, Turkish-German University, Istanbul, Turkey

    Dilek Göksel Duru

  2. Information Technologies, Graduate School of Science and Engineering, Altınbaş University, Istanbul, Turkey

    May Alobaidi

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  1. Dilek Göksel Duru
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Correspondence to Dilek Göksel Duru.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Ethics Committee of Istanbul Arel University approval number 2019/07/no.12.

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Göksel Duru, D., Alobaidi, M. Classification of brain electrophysiological changes in response to colour stimuli. Phys Eng Sci Med 44, 727–743 (2021). https://doi.org/10.1007/s13246-021-01021-2

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